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Joint Antenna and Propagation Model Parameter Estimation Using RSS Measurements Joint Antenna and Propagation Model Parameter Estimation using RSS measurements Parinaz Kasebzadeh, Carsten Fritsche, Emre Özkan, Fredrik Gunnarsson and Fredrik Gustafsson Linköping University Post Print N.B.: When citing this work, cite the original article. Original Publication: Parinaz Kasebzadeh, Carsten Fritsche, Emre Özkan, Fredrik Gunnarsson and Fredrik Gustafsson, Joint Antenna and Propagation Model Parameter Estimation using RSS measurements, 2015, 18th International Conference on Information Fusion, Washington D.C, USA, July 6-9, 2015. ©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Postprint available at: Linköping University Electronic Press http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-120962 Joint Antenna and Propagation Model Parameter Estimation using RSS measurements Parinaz Kasebzadeh, Carsten Fritsche, Emre Ozkan,¨ Fredrik Gunnarssony, Fredrik Gustafsson Department of Electrical Engineering, Linkoping¨ University, Linkoping,¨ Sweden Email: [email protected], fcarsten, emre, [email protected] y Ericsson Research, Linkoping,¨ Sweden, Email: [email protected] Abstract—In this paper, a semi-parametric model for RSS management perspective, the positioned radio measurement measurements is introduced that can be used to predict coverage enables an operator to identify where issues such as poor in cellular radio networks. The model is composed of an empirical coverage or excessive interference are located. Commonly, log-distance model and a deterministic antenna gain model that accounts for possible non-uniform base station antenna radiation. one distinguish between network-centric and mobile-centric A least-squares estimator is proposed to jointly estimate the path solutions. In the former, a network entity estimates the position loss and antenna gain model parameters. Simulation as well as of a terminal, possibly based on measurements reported by experimental results verify the efficacy of this approach. The the terminal. Moreover, in the latter, the terminal is provided method can provide improved accuracy compared to conventional with assistance data to enable it to estimate its position. path loss based estimation methods. The position estimate may also be based on a measurement snapshot, or a time series of measurements. The measurements I. INTRODUCTION are typically related to time of arrival (TOA), received signal The high pace of development of geographical positioning strength (RSS) and angle of arrival (AoA) of transmitted methods along with tracking mobile users might be routed reference signals, or combinations thereof [12]. to various sources. Navigation, people and assets tracking, RSS measurements are typically reported from the terminal location based security and coordination of emergency and to the base station for other reasons than positioning, such as maintenance responses to accidents, interruptions of essential handover from a serving cell to a target cell, radio resource services, mapping the location of disaster victims, cellular management in general, or to assess the properties of the system design and management are examples of different radio conditions in a cell as part of network management. applications and services which rely on the accurate position Therefore, they can be seen as readily available. In a po- estimation [1]. sitioning context, RSS measurements are used for ranging The first enabling Global Navigation Satellite System and fingerprinting [12]. Recently, it has also been shown (GNSS)-based positioning methods such as those based on how RSS measurements together with information about base Global Positioning System (GPS) are still in use in many station antenna properties, can be used to estimate the angle applications. Briefly speaking, those methods are based on of arrival [13]. Also, knowledge about the serving sector cell signals transmitted from satellites and as mentioned are a together with information about the sectorized antenna, yields primary source of location estimations for many applications. a crude estimate of the angle of arrival for the terminal. The However, the negative-affecting factors impose the necessity performance of positioning based on such bearing estimates of evolution of conventional approaches. For example, outdoor is assessed in [14], and in [12]. The latter also address the environments such as ”urban canions”, bad weather conditions positioning performance given RSS measurements used for resulting in poor signals, and indoor environments where the ranging, given a parametric radio propagation model. signal is totally non-accessible, other alternatives must be Two main approaches that are widely studied in the liter- applied. Assisting GNSS signals with other wireless networks ature that use RSS measurements for positioning purposes, such as the Third Generation Partnership Project (3GPP), are channel modeling [15], [16] and fingerprinting [17], [18]. Long Term Evolution (LTE) or IEEE 802.11, that is studied Both of these two however, suffer from lack of considering widely in the literature (for different applications, see [2]– simultaneous effects of channel and antenna parameters on [4]), is one solution to increase both availability of service RSS measurements they use for positioning algorithms. More- and the accuracy of estimation. On the other hand, in indoor over, the change of channel and antenna parameters based environments, relying on available Wireless LAN (WLAN) on the instantaneous propagation condition is also neglected. infrastructures for position estimation purposes is investigated If detailed propagation model calibration is ruled out, the for a long term where, [5]–[11] can be seen as examples. applicability of the RSS measurements for ranging is subject Cellular radio network positioning can be seen as an alter- to significant uncertainty. Such uncertainty can be considered native to Assisted GPS (A-GPS) systems mentioned above, in the positioning algorithm, but still with fairly inaccurate when the latter is unavailable. Example use cases include positioning performance as a result [19]. emergency call positioning. Furthermore, from a radio network An alternative is to take advantage of the recent devel- opment of smartphones, capable of logging accurate GPS the MS position (antenna gain model). We further make positions and RSS measurements. Thereby, it is possible the common assumption that the RSS measurement is time- to retrieve such trajectories at a calibration entity and use averaged, such that temporal effects resulting from small-scale for model parameter calibrations. Traditionally, close range fading can be neglected. Hence, the proposed semi-empirical measurements have been used to measure the antenna gain in model for the RSS measurement in logarithmic scale (dBm) detail, while avoiding significant propagation effects. More- can be written as over, positioned RSS measurements have been modified in y = P − fL(x) − G (x)g + e; (2) consideration of the antenna model to determine corresponding T ant T RSS measurements from an isotropic antenna, which in turn where x , [d; φ, ] is the vector holding the relative position has been used to estimate the parameters of the propagation information of the MS with respect to the BS antenna, PT is model. the BS transmit power in dBm, and e is a statistical term which In this paper, we present a method to jointly estimate both accounts for the errors resulting from quantization, slow fading the antenna and propagation model parameters using posi- and other effects that are not captured by the propagation tioned RSS measurements. The rest of the paper is organized model. The error term e is modeled with a zero-mean Gaussian as follows: In Section II a semi-empirical model for RSS distribution with variance σ2. measurement is presented. Section III addresses requirements for joint propagation model and antenna parameter estima- A. Path Loss Model tion representation. Section IV evaluates performance of the A path loss (PL) model presents signal attenuation in space. proposed model using simulations followed by verifications In this work, the log-distance model is used as it forms the applied on real-field data provided in Section V. Finally, basis of most models available in the literature [22], [5]. The Section VI concludes the work. log-distance model is given by d II. RSS MEASUREMENT MODEL L(d) = A + 10B log ; (3) 10 d The RSS measurement y can be expressed by the following 0 general model where A is the reference path loss, B is the path loss exponent, d is the Euclidean distance between the MS and BS, and d0 y = h(x) + e; (1) represents the distance at which the reference path loss A is where h(x) is a propagation model (sometimes also called determined. The value of d0 generally depends on the cell size, radio channel) that accounts for propagation effects, such as and values that can be typically found are 100 m or 1 km. attenuation, diffraction, or reflection, that the electromagnetic B. Antenna Gain Model wave is affected by, when traveling between the Mobile Station Base station antenna modeling mainly concerns crude mod- (MS) and the Base Station (BS). Here, x denotes a (vector) els that capture the far-field (at some distance from the variable providing
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